Anyone Using Living Brain? (livingbrain.com)

Living Brain has introduced two innovative approaches for AI adaptation that enhance model functionality without altering their underlying weights. The first method allows models to adapt in real time by storing knowledge, corrections, and decisions from past tasks. By retaining this context, Living Brain enables models—regardless of whether they are open or closed-weight—to better align with user-specific tasks during inference, thus improving their responsiveness and relevance. The second path leverages memory as a foundation for post-training processes. It allows for the accumulation of knowledge, feedback, and outcomes that can be systematically organized into training examples and evaluation datasets. This curated information can facilitate fine-tuning and enhance performance in open-weight models, as well as provide valuable training opportunities for closed-weight models where access is permitted. This dual approach not only pushes the boundaries of AI adaptability but also ensures models can continuously evolve based on user needs and experiences, marking a significant advancement for the AI/ML community.